docs: def count(self, data):
          """ Compute histogram of data.
  
          Counts the number of elements from array ``data`` in each bin of the
          histogram. Results are returned in an array, call it ``h``, of
          length ``nbin+2`` where ``h[0]`` is the number of data elements
          that fall below the range of the histogram, ``h[-1]``
          (i.e., ``h[nbin+1]``) is the number that fall above the range,
          and ``h[i]`` is the number in the ``i``-th bin for ``i=1...nbin``.
  
          Argument ``data`` can also be a float, in which case the result is the
          same as from ``histogram([data])``. Note that the expectation value of
          ``count(f(p))`` over parameter values ``p`` drawn from a random
          distribution gives the probabilities for values of ``f(p)`` to fall
          in each histogram bin. Dividing  by the bin widths gives the average
          probability density for random variable ``f(p)`` in each bin.
  
          Bin intervals are closed on the left and open on the right,
          except for the last interval which is closed on both ends.
          """
          if isinstance(data, float) or isinstance(data, int):
              hist = numpy.zeros(self.nbin + 2, float)
              if data > self.bins[-1]:
                  hist[-1] = 1.
              elif data < self.bins[0]:
                  hist[0] = 1.
              elif data == self.bins[-1]:
                  if self.nbin > 1:
                      hist[-2] = 1.
              else:
                  hist[numpy.searchsorted(self.bins, data, side='right')] = 1.
              return hist
          if numpy.ndim(data) != 1:
              data = numpy.reshape(data, -1)
          else:
              data = numpy.asarray(data)
          middle = numpy.histogram(data, self.bins)[0]
          below = numpy.sum(data < self.bins[0])
          above = numpy.sum(data > self.bins[-1])
          return numpy.array([below] + middle.tolist() + [above], float)